Dokuz Eylül University Information Package / Courses Catalog

Description of Individual Course Units

Course Unit Code	Course Unit Title	Type Of Course	D	U	L	ECTS
		ELECTIVE

Offered By

Industrial Engineering

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR FEHMI BURÇIN ÖZSOYDAN

Offered to

Industrial Engineering

Course Objective

With this proposed course, it is aimed to explain to DEU Industrial Engineering Department students how data, which has an extremely important place in engineering science and real life problems, can be used and how information can be produced from data, using data mining and machine learning methods. With this course, our students will be given basic information about data mining methods and in-class applications will be made with the help of the free data mining software Weka. Within the scope of the course, studies will be made on data mining and machine learning approaches, which are the main sub-titles of artificial intelligence.

Learning Outcomes of the Course Unit

1	Being able to understand and apply data mining approaches.
2	Ability to filter and visualize data.
3	Ability to apply basic data mining techniques with Weka.
4	Understanding and applying machine learning.
5	Ability to use basic data mining and machine learning algorithms.
6	Obtaining information on legal liability and ethics in data mining approaches.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week	Subject	Description
1	Introduction to data mining
2	Weka software introduction
3	Filtering, data visualization and data preprocessing on Weka
4	Studying on classifiers, concept of baseline accuracy
5	Cross validation
6	Simple classifiers and concept of overfitting
7	Midterm
8	Decision trees
9	Nearest neighbourhood algorithm
10	Classification and linear regression
11	Classification with regression and logistic regression
12	Clustering
13	Support vector machines
14	Data mining process, pitfalls, ethics, period review

Recomended or Required Reading

Witten, Ian H., Eibe Frank, and A. Mark. "Hall, and Christopher J Pal. 2016. Data Mining: Practical machine learning tools and techniques.", ISBN: 978-0128042915

Planned Learning Activities and Teaching Methods

The topics covered in the course will be transferred to the students through computer-based applications, sample problem solutions and presentations on the board and students will be expected to perform these applications. The course will involve intensive coding. In addition, all the techniques described in this course will be brought together and used.

Assessment Methods

SORTING NUMBER	SHORT CODE	LONG CODE	FORMULA
1	MTE	MIDTERM EXAM
2	PRJ	PROJECT
3	FIN	FINAL EXAM
4	FCG	FINAL COURSE GRADE	MTE * 0.30 + PRJ * 0.20 + FIN * 0.50
5	RST	RESIT
6	FCGR	FINAL COURSE GRADE (RESIT)	MTE * 0.30 + PRJ * 0.20 + RST * 0.50

Further Notes About Assessment Methods

None

Assessment Criteria

Midterm (30%) + Project (20%) + Final (50%)

Language of Instruction

Turkish

Course Policies and Rules

This course will mainly focus on data mining applications.

Contact Details for the Lecturer(s)

Adress: Dokuz Eylül University, Industrial Engineering Department, Tınaztepe Campus, Izmir, Türkiye

E-mail: burcin.ozsoydan@deu.edu.tr, burcin.ozsoydan@gmail.com

Tel: 0232 301 7630

Office Hours

Different office hours are determined each semester.

Work Placement(s)

None

Workload Calculation

Activities	Number	Time (hours)	Total Work Load (hours)
Lectures	12	3	36
Preparations before/after weekly lectures	12	1	12
Preparation for midterm exam	1	15	15
Preparation for final exam	1	20	20
Preparing presentations	1	15	15
Final	1	2	2
Midterm	1	2	2
TOTAL WORKLOAD (hours)			102

Contribution of Learning Outcomes to Programme Outcomes

PO/LO	PO.1	PO.4	PO.5
LO.1	5	5	5
LO.2			5
LO.3	5	5	5
LO.4		5	5
LO.5		5	5
LO.6

COURSE UNIT TITLE

DEGREE PROGRAMMES

Description of Individual Course Units

Offered By

Level of Course Unit

Course Coordinator

Offered to

Course Objective

Learning Outcomes of the Course Unit

Mode of Delivery

Prerequisites and Co-requisites

Recomended Optional Programme Components

Course Contents

Recomended or Required Reading

Planned Learning Activities and Teaching Methods

Assessment Methods

Further Notes About Assessment Methods

Assessment Criteria

Language of Instruction

Course Policies and Rules

Contact Details for the Lecturer(s)

Office Hours

Work Placement(s)

Workload Calculation

Contribution of Learning Outcomes to Programme Outcomes

CONTACT INFORMATION